blocks|key|127820|text|正如@EvanWright在评论中所解释的那样，|type|unstyled|depth|inlineStyleRanges|entityRanges|data|127821|data['S1Q2I']+=+pd.to_numeric(data['S1Q2I'])|code-block|syntax|javascript|127822|是现在转换类型的首选方式。有关更改的详细说明，请参阅github+PR+GH11133。|offset|length|127823|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/pydata/pandas/pull/11133^0|0|0|10|7|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@]|9|@$I|V|J|W|1|X]]|A|$]]|$1|K|3|-4|5|6|7|Y|8|@]|9|@]|A|$]]]|L|$M|$5|N|O|P|A|$Q|R]]]]

As explained by @EvanWright in the comments,

<pre><code>data['S1Q2I'] = pd.to_numeric(data['S1Q2I'])
</code></pre>

is now the prefered way of converting types. A detailed explanation in of the change can be found in the github PR <a href="https://github.com/pydata/pandas/pull/11133" rel="noreferrer">GH11133</a>.

blocks|key|2896378|text|您可以使用apply+as+done+here实现替换。下面是一个例子：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|2896379|>>>+import+pandas+as+pd
>>>+a+=+pd.DataFrame([{"letter":"a",+"number":"1"},{"letter":"b",+"number":"2"}])
>>>+a.dtypes
letter++++object
number++++object
dtype:+object
>>>+b+=+a.apply(pd.to_numeric,+errors="ignore")
>>>+b.dtypes
letter++++object
number+++++int64
dtype:+object
>>>+|code-block|syntax|javascript|2896380|但它在两个方面很糟糕：|2896381|2896382|，你必须使用apply，而不是一个非原生数据帧方法，|ordered-list-item|2896383|，你必须拷贝到另一个数据帧--不能就地完成。关于“大数据”的使用就这么多了。|2896384|2896385|我真的不喜欢熊猫的发展方向。我不经常使用R+data.table，但到目前为止它似乎更好。|2896386|我认为具有原生、就地类型转换的数据表对于一个具有竞争力的数据分析框架来说是非常基本的。|2896387|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/33795876/188963^0|J|4|0|0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|13|8|@]|9|@$A|14|B|15|1|16]]|C|$]]|$1|D|3|E|5|F|7|17|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|18|8|@]|9|@]|C|$]]|$1|K|3|-4|5|6|7|19|8|@]|9|@]|C|$]]|$1|L|3|M|5|N|7|1A|8|@]|9|@]|C|$]]|$1|O|3|P|5|N|7|1B|8|@]|9|@]|C|$]]|$1|Q|3|-4|5|6|7|1C|8|@]|9|@]|C|$]]|$1|R|3|S|5|6|7|1D|8|@]|9|@]|C|$]]|$1|T|3|U|5|6|7|1E|8|@]|9|@]|C|$]]|$1|V|3|-4|5|6|7|1F|8|@]|9|@]|C|$]]]|W|$X|$5|Y|Z|10|C|$11|12]]]]

You can effect a replacement using apply as done <a href="https://stackoverflow.com/a/33795876/188963">here</a>. An example would be:

<pre><code>&gt;&gt;&gt; import pandas as pd
&gt;&gt;&gt; a = pd.DataFrame([{"letter":"a", "number":"1"},{"letter":"b", "number":"2"}])
&gt;&gt;&gt; a.dtypes
letter object
number object
dtype: object
&gt;&gt;&gt; b = a.apply(pd.to_numeric, errors="ignore")
&gt;&gt;&gt; b.dtypes
letter object
number int64
dtype: object
&gt;&gt;&gt; 
</code></pre>

But it sucks in two ways:

<ol>
<li>You have to use apply rather than a non-native dataframe method</li>
<li>You have to copy to another dataframe--can't be done in place. So much for use with "big data."</li>
</ol>

I'm not really loving the direction pandas is going. I haven't used R data.table much, but so far it seems superior.

I think a data table with native, in-place type conversion is pretty basic for a competitive data analysis framework.

blocks|key|2684408|text|您可以让它正确地应用于数据帧中的特定变量名，而不必复制到不同的数据帧中，如下所示：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2684409|>>>+import+pandas+as+pd
>>>+a+=+pd.DataFrame([{"letter":"a",+"number":"1"},{"letter":"b",+"number":"2"}])
>>>+a.dtypes
letter++++object
number++++object
dtype:+object
>>>+a['number']+=+a['number'].apply(pd.to_numeric,+errors='coerce')
>>>+a.dtypes
letter++++object
number+++++int64
dtype:+object|code-block|syntax|javascript|2684410|基于上述原始问题的示例如下所示：|2684411|data['S1Q2I']+=+data['S1Q2I'].apply(pd.to_numeric,+errors='coerce')|2684412|它的工作方式与您的原始文件相同：|2684413|data['S1Q2I']+=+data['S1Q2I'].convert_objects(convert_numeric=True)|2684414|不管怎样，在我的手中……|2684415|这并没有解决abalter提出的关于推断数据类型的问题，恐怕这有点超出了我的理解！|2684416|entityMap^0|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|W|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|Y|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|Z|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|10|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|11|8|@]|9|@]|A|$]]|$1|S|3|-4|5|6|7|12|8|@]|9|@]|A|$]]]|T|$]]

You can get it to apply correctly to a particular variable name in a dataframe without having to copy into a different dataframe like this: 

<pre><code>&gt;&gt;&gt; import pandas as pd
&gt;&gt;&gt; a = pd.DataFrame([{"letter":"a", "number":"1"},{"letter":"b", "number":"2"}])
&gt;&gt;&gt; a.dtypes
letter object
number object
dtype: object
&gt;&gt;&gt; a['number'] = a['number'].apply(pd.to_numeric, errors='coerce')
&gt;&gt;&gt; a.dtypes
letter object
number int64
dtype: object
</code></pre>

An example based on the original question above would be something like:

<pre><code>data['S1Q2I'] = data['S1Q2I'].apply(pd.to_numeric, errors='coerce')
</code></pre>

This works the same way as your original:

<pre><code>data['S1Q2I'] = data['S1Q2I'].convert_objects(convert_numeric=True)
</code></pre>

in my hands, anyway.... 

This doesn't address the point abalter made about inferring datatypes which is a little above my head I'm afraid!

blocks|key|2684426|text|这取决于Pandas的哪个版本......如果您使用的是Pandas的0.18.0版本，则此类型将正常工作........|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2684427|df['col+name']+=+df['col+name'].apply(pd.to_numeric,+errors='coerce')+|code-block|syntax|javascript|2684428|另一个版本........|2684429|df['col+name']=df.col+name+.astype(float)|2684430|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

It depends on which version of Pandas......
if you have Pandas's version 0.18.0 
this type will work ........

<pre><code>df['col name'] = df['col name'].apply(pd.to_numeric, errors='coerce') 
</code></pre>

another versions ........ 

<pre><code>df['col name']=df.col name .astype(float)
</code></pre>

blocks|key|2896414|text|如果您一次将所有列转换为数字，则此代码可能会起作用。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2896415|data+=+data.apply(pd.to_numeric,+axis=0)|code-block|syntax|javascript|2896416|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

If you convert all columns to numeric at once, this code may work.

<pre><code>data = data.apply(pd.to_numeric, axis=0)
</code></pre>

I have this line in my code which converts my data to numeric...

<pre><code>data["S1Q2I"] = data["S1Q2I"].convert_objects(convert_numeric=True)
</code></pre>

The thing is that now the new pandas release (0.17.0) said that this function is deprecated..
This is the error:

<pre><code>FutureWarning: convert_objects is deprecated. 
Use the data-type specific converters pd.to_datetime, 
pd.to_timedelta and pd.to_numeric. 
data["S3BD5Q2A"] = data["S3BD5Q2A"].convert_objects(convert_numeric=True)
</code></pre>

So, I went to the new documentation and I couldn't find any examples of how to use the new function to convert my data...

It only says this:

<blockquote>
 "DataFrame.convert_objects has been deprecated in favor of type-specific functions pd.to_datetime, pd.to_timestamp and pd.to_numeric (new in 0.17.0) (GH11133)."
</blockquote>

Any help would be nice!

Pandas ".convert_objects(convert_numeric=True)" deprecated

数据分析

数据转换

大数据

我在我的代码中有这一行，它将我的数据转换为数字...data["S1Q2I"] = data["S1Q2I"].convert_objects(convert_numeric=True)事情是这样的，现在新的pandas版本(0.17.0)说这个函数被弃用了。这是错误：FutureWarning: convert_ob...

问熊猫".convert_objects(convert_numeric=True)“已弃用
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